MultiMAE: An Inspiration to Leverage Labeled Data in Unsupervised Pre-training | by Shuchen Du | Jul, 2022
Boost your model performance via multimodal masked auto-encodersPhoto by Pablo Arenas on UnsplashSelf-supervised pre-training is a main approach to improve the performance to traditional supervised learning, in which large amount of labeled data is necessary and costly. Among self-supervised learning methods, contrastive learning is popular for its simplicity and efficacy. However, most contrastive learning methods use global vectors in which the details of pixel-level information is lost, which leaves room of improvement…